PDC {FPDclustering} | R Documentation |
Probabilistic Distance Clustering
Description
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD clustering is based on the constraint that the product of the probability and the distance of each point to any cluster centre is a constant.
Usage
PDC(data = NULL, k = 2)
Arguments
data |
A matrix or data frame such that rows correspond to observations and columns correspond to variables. |
k |
A numerical parameter giving the number of clusters |
Value
A class FPDclustering list with components
label |
A vector of integers indicating the cluster membership for each unit |
centers |
A matrix of cluster centers |
probability |
A matrix of probability of each point belonging to each cluster |
JDF |
The value of the Joint distance function |
iter |
The number of iterations |
data |
the data set |
Author(s)
Cristina Tortora and Paul D. McNicholas
References
Ben-Israel C. and Iyigun C. Probabilistic D-Clustering. Journal of Classification, 25(1), 5-26, 2008.
Examples
#Normally generated clusters
c1 = c(+2,+2,2,2)
c2 = c(-2,-2,-2,-2)
c3 = c(-3,3,-3,3)
n=200
x1 = cbind(rnorm(n, c1[1]), rnorm(n, c1[2]), rnorm(n, c1[3]), rnorm(n, c1[4]) )
x2 = cbind(rnorm(n, c2[1]), rnorm(n, c2[2]),rnorm(n, c2[3]), rnorm(n, c2[4]) )
x3 = cbind(rnorm(n, c3[1]), rnorm(n, c3[2]),rnorm(n, c3[3]), rnorm(n, c3[4]) )
x = rbind(x1,x2,x3)
#Clustering
pdn=PDC(x,3)
#Results
plot(pdn)